Efficient sequential fuzzy-neural algorithms for aircraft fault-tolerant control

Efficient sequential fuzzy-neural algorithms for aircraft fault-tolerant control

This thesis focuses on the development of two efficient sequential fuzzy neural algorithms. The first algorithm is named as Sequential Adaptive Fuzzy Inference System where the number of fuzzy rules is determined automatically according to the learning procedure and the parameters in the existing fu...

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Bibliographic Details
Main Author: Rong, Haijun
Other Authors: Narasimhan Sundararajan
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
DRNTU::Engineering::Aeronautical engineering::Air navigation
Online Access:https://hdl.handle.net/10356/3507
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Institution: Nanyang Technological University
Description
Summary:This thesis focuses on the development of two efficient sequential fuzzy neural algorithms. The first algorithm is named as Sequential Adaptive Fuzzy Inference System where the number of fuzzy rules is determined automatically according to the learning procedure and the parameters in the existing fuzzy rules are modified. The second algorithm is called as On-line, Sequential, Fuzzy Extreme Learning Machine where the parameters for the fuzzy rules are updated at an extremely high speed. Besides based on the two new fuzzy neural algorithms, two adaptive, fault-tolerant, fuzzy control strategies are developed in this thesis for a high performance fighter automatic landing problem under the failures of stuck control surfaces and severe winds.

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